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Record W2155280140 · doi:10.1109/icc.2006.255766

A Cooperative Game Framework for Bandwidth Allocation in 4G Heterogeneous Wireless Networks

2006· article· en· W2155280140 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2006 IEEE International Conference on Communications · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceComputer networkBandwidth allocationShapley valueWireless networkChannel allocation schemesBandwidth (computing)Resource allocationWirelessDistributed computingResource management (computing)Game theoryTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

One of the most important features of the evolving fourth generation (4G) wireless networks is the capability of a mobile station to connect to several wireless access networks simultaneously. This introduces new challenges in bandwidth allocation among mobiles since the load characteristics of different networks must be taken into account to design efficient resource allocation algorithms. In this paper, we present bandwidth allocation and admission control algorithms based on bankruptcy game which is a special type of an N-person cooperative game. A coalition among the different wireless access networks is formed to offer bandwidth to a new connection. The stability of the allocation is analyzed by using the concept of the core and the amount of allocated bandwidth to a connection in each network is obtained by using Shapley value. Subsequently, an admission control algorithm is proposed. Numerical results are presented to demonstrate the behaviors of the proposed algorithms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.307
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it